Migrating from Azure Speech to Gladia: a step-by-step switching guide

TL;DR: Migrating from Azure Speech to Gladia removes the overhead of custom training pipelines and fragmented per-feature billing. Azure routes diarization, translation, and sentiment through separate services with separate billing meters. We bundle all audio intelligence into one per-hour rate on Starter and Growth plans. Solaria-3 ranks #1 for real-world European business audio, Solaria-1 covers 100+ languages with native code-switching. Both deliver out-of-the-box accuracy that eliminates custom training for most production audio. Most engineering teams complete the API refactoring in under 24 hours.

How to evaluate a speech-to-text API: a technical buyer's framework

TL;DR: Choosing an STT API on vendor benchmarks alone is how teams end up with transcription that looks fine in staging and breaks on production audio. A rigorous evaluation requires building a test set from your own calls, measuring word error rate (WER) on your specific audio distribution, stress-testing latency under concurrent load, and auditing data retraining terms before signing. This guide gives you a reusable engineering blueprint to run that evaluation end-to-end, the same methodology behind our own open async benchmark, which covers 7 datasets and 74+ hours of audio across 8 providers.

The contact center QA scorecard: what to measure and how transcription feeds it

TL;DR: Manual QA teams review as little as 1% to 2% of contact center calls, leaving the vast majority of interactions unreviewed and exposing systemic compliance risks that sampling never surfaces. Scaling to automated coverage requires transcription accurate enough to power LLM-based scoring without silent failures. If your speech-to-text engine misattributes a speaker or drops a compliance disclosure, every downstream scorecard, CRM entry, and coaching flag is wrong. French CCaaS platform Gravite cut per-call review time from 15 minutes to 1 minute (93% reduction) while automating coverage across their full 50,000 hours of annual call volume on infrastructure built for real-world contact center audio.

HDS and French healthcare data residency for voice transcription

TL;DR: Standard GDPR compliance does not satisfy French law for patient voice data. Article L.1111-8 of the French Public Health Code mandates HDS certification for every subcontractor in your transcription pipeline, and the May 16, 2026 HDS v2.0 transition deadline has invalidated all v1.1 certificates. We provide HDS-certified speech-to-text on dedicated EU cloud clusters, combining strict compliance with Solaria-3, our model built for noisy, accented European business audio and validated for clinical use with custom vocabulary support. This guide covers legal requirements, data residency boundaries, and a procurement checklist for compliant voice pipelines.

Microsoft Teams transcription via API

TL;DR: Native Microsoft Teams transcription via the Graph API delivers transcripts only after a meeting ends, provides utterance-level (not word-level) timestamps, and degrades sharply on accented or multilingual speech. For any product that routes Teams audio to downstream AI systems, the more reliable architectural pattern is capturing raw audio via a custom WebRTC bot and routing it to a managed STT engine, one that delivers word-level timestamps, accurate multilingual handling, and predictable per-hour costs, none of which the native Graph API provides. Solaria-1 covers real-time streaming and broad language support. Solaria-3 is optimised for European business audio.

Podcast transcription at scale: an API workflow for media platforms

TL;DR: Podcast audio is invisible to search without accurate, word-level transcripts, and transcription quality sets the ceiling for everything downstream, from content discovery to AI-generated show notes. A production-grade async pipeline (decoupled webhook ingestion, pyannoteAI-powered diarization, word-level timestamps) is what separates a searchable audio library from a title-and-description catalog. At 10,000 hours monthly, a managed API costs $2,000–$6,100 depending on plan.

HIPAA-ready meeting assistants for healthcare and therapy sessions

TL;DR: Building a HIPAA-ready meeting assistant requires more than a generic transcription wrapper. Any API that processes Protected Health Information on your behalf must sign a Business Associate Agreement (BAA) before PHI flows to it, and transcription accuracy matters more than most teams expect: word error rate can more than double in noisy, multi-speaker clinical environments compared to controlled recordings, meaning errors compound into every SOAP note and EHR entry downstream. This guide covers the BAA requirements, encryption controls, and unit economics product teams need to evaluate before committing to an audio infrastructure provider for clinical or therapy use cases.

Migrating from AWS Transcribe to Gladia: a step-by-step switching guide

TL;DR: Migrating from AWS Transcribe to Gladia removes three steps from your audio pipeline (S3 staging, IAM configuration, and polling loops), replacing them with a single POST request and native webhook delivery. Most AWS parameters translate directly to our request body with no separate compilation or activation steps. For teams processing noisy, accented, or multilingual business audio, Solaria-3 is built specifically for European real-world business audio, while Solaria-1 covers 100+ languages and real-time streaming.

Best Wispr Flow alternatives in 2026

Every dictation app demo looks the same: someone talks, words appear, everyone's impressed. What separates these tools only shows up after months of daily use: what it costs once the free tier runs out, whether your audio ever leaves your machine, whether you're locked into someone else's server just to type into your own apps. Wispr Flow is the app most people mean when they search for AI dictation software, and it earned that reputation fair and square. It's also a $144-a-year subscription, cloud-only with no offline mode, and closed-source, which is why this list exists.